How Case Law AI Tools Actually Work (Part 2): Legal AI vs. ChatGPT and Evaluating Accuracy
In Part 1, we covered how legal AI processes information using semantic search and RAG architecture. Now let's look at what makes legal AI fundamentally different from general-purpose tools like ChatGPT, and how to evaluate the accuracy claims vendors make.
What Makes Legal AI Different From ChatGPT
Purpose-Built vs. General-Purpose AI
ChatGPT is trained on internet text—Wikipedia articles, blog posts, forum discussions, and scraped web content. Legal AI is trained on case law, statutes, legal treatises, and court documents. This distinction fundamentally changes how they process legal queries.
General-purpose AI hallucinates citations because it's predicting plausible-sounding text based on patterns it observed during training. It "knows" that legal writing includes citations in specific formats, so it generates text that looks like a citation—but the case doesn't exist. It's not lying; it's doing exactly what it was designed to do: predict the next most likely sequence of characters.
Legal-specific AI understands precedent weight, jurisdiction hierarchy, and citation formats because it was trained exclusively on legal documents where these concepts matter. It recognizes that a Supreme Court decision carries more weight than a district court opinion, that en banc circuit decisions bind panel decisions, and that overruled cases shouldn't be cited as good law.
When might general AI still be useful? Brainstorming case theories, explaining complex legal concepts to clients in plain language, or drafting non-legal content like firm newsletters. But for case law research, citation verification, or anything that requires legal accuracy, purpose-built legal AI is non-negotiable.
Citation Verification: The Critical Difference
Legal platforms verify citations exist and are accurately quoted through multiple mechanisms. Some use automated cross-referencing against their legal database, checking that the case exists, the citation format is correct, and the quoted language actually appears in the opinion.
The hallucination problem is real and well-documented. Generic AI will confidently cite "Smith v. Jones, 123 F.3d 456 (9th Cir. 2023)" when no such case exists. It generates these fake citations because it learned the pattern of what citations look like, not because it's retrieving actual cases from a database.
What "citation checking" actually means varies dramatically by platform. Some vendors claim their AI "checks citations" when they really mean it verifies the format looks correct—not that the case exists or is quoted accurately. Others perform genuine verification against their legal database, confirming both existence and accuracy.
Red flags include platforms that don't explain their verification process, claim "99% accuracy" without defining what they're measuring, or suggest you can rely on AI research without independent verification.
Evaluating Accuracy: What Vendors Won't Tell You
The Hallucination Rate Question
Vendors rarely publish accuracy metrics, and that silence tells you something important. When pressed, many will deflect with claims about "proprietary testing" or "continuous improvement" without providing actual numbers. What percentage of citations are verified accurate? How do you measure hallucination rates? These questions often go unanswered.
During trials, run your own accuracy checks using research you've already completed. Take a recent matter where you know the relevant case law, input the same query into the AI tool, and compare results. Are the cases it finds actually relevant? Are citations accurate? Does it miss key precedent you know exists?
Realistic expectations matter: even the best tools require human verification. Legal AI should reduce your research time, not eliminate your professional judgment.
See how Lucio handles citation accuracy — book a demo
Database Coverage Transparency
Ask vendors specifically which jurisdictions are fully covered versus partially covered. "We cover all federal courts" might mean published appellate decisions but not district court orders. "Comprehensive state law coverage" might exclude unpublished opinions or administrative decisions.
Historical depth varies enormously. Some platforms index cases back to the 1800s; others start in 1950 or later. If you practice appellate law or need historical precedent for constitutional arguments, this gap matters.
Unpublished opinions and trial court decisions are often missing or incomplete, yet they're frequently the most relevant precedent for specific procedural issues or evidentiary rulings. Verify coverage claims by asking for specific examples in your practice area.
Quality Control Workflows You Need
Never trust AI output without verification. Establish a step-by-step process: First, confirm cited cases exist by checking them in your primary legal database. Second, verify quoted language is accurate—not paraphrased or taken out of context. Third, confirm cases remain good law using Shepard's or KeyCite. Fourth, assess whether the case actually supports the proposition for which you're citing it.
Training your team means everyone using AI needs the same verification standards. One associate cutting corners on citation checking creates liability for the entire firm.
In Part 3, we cover how these tools fit into real legal workflows and how to choose the right platform for your practice.